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investigate the feasibility of new imaging modalities for situations where currently employed imaging techniques, such as X-ray transmission and backscatter, have limitations. This project will focus
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will lead to natural collaboration opportunities. The primary methods used in this project will be experimental, involving fluid characterisation and high-speed imaging experiments, using Phantom high
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We invite applications for a fully funded PhD research scholarship in “Unsupervised Machine Learning for Cardiovascular Image Analysis”. This opportunity is available to UK (Home) candidates only
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. You will be responsible for carrying out research on neuroimaging meta-analysis (NMA), developing novel methods and implementing them in efficient, well-documented and user-friendly code to be
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on neuroimaging meta-analysis (NMA), developing novel methods and implementing them in efficient, well-documented and user-friendly code to be integrated with the NiMARE (NMA) software project. To be considered you
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+ algorithms/AI for optical imaging, and clinical translation (more at www.georgesdgordon.com). You will be supported by colleagues with strong skills in building optical systems, coding and data analytics
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device longevity (37million cardiac cycles per year!), whilst ensuring optimal cardiovascular function for the patient. This project proposes to integrate state-of-the-art image-based measurement
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changes in behaviour. In parallel, human epilepsy patients with electrodes inserted into the amygdala will view many of the same images presented to monkeys. This will allow us to determine the extent to
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enzyme with implications in cancer and neurological diseases. The student will carry out cryo-EM imaging of human MTHFR titrated with its native allosteric inhibitor S-adenosyl-l-methionine (SAM
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summary Many important applications in materials science and medical imaging rely on high detail image slices / scans which are compared across time (e.g. via micro x-ray CT). The technology to compare